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Improving the Performance of Analytics
Use data to increase productivity and make better decisionsPreview the first lesson free — get full access to all 5 lessons.
Intermediate Provider : Mary Margaret Chantré 5 Lessons · 11m in Arabic, German, English, Spanish, French, Portuguese, Chinese
Course Description
Business analytics is a powerful tool that can be used to make decisions and build business strategies. Organizations today generate vast amounts of data. Businesses use that data to improve efficiency and productivity, make better business decisions, and increase profitability. This is where predictive analytics can be useful, but it works best when constantly being tested, checked, and improved. Randomization and parallelization are two of the most important techniques used in predictive analytics.
In these lessons, you’ll learn to optimize the performance of algorithms and models. You’ll also learn vital concepts of gradient, descent, and intuition, which can be used to improve the performance of predictive algorithms. We’ll also discuss assessing predictive models, which is essential when building and deploying machine learning models. We’ll look at several ways to assess predictive models, including cross-validation, holdout validation, and A/B testing.
What You'll Learn
- Optimize randomization and parallelization to improve algorithm performance
- Identify tools for optimizing the performance of machine learning
- Apply the concepts of gradient, descent, and intuition to improve predictive algorithms
- Assess predictive models using cross-validation, holdout validation, and A/B testing
- Explain predictive models and their role in deployment
Key Takeaways
- Business analytics uses an organization's data to improve efficiency and productivity, make better decisions, and increase profitability.
- Predictive analytics works best when it is constantly being tested, checked, and improved.
- Randomization and parallelization are two of the most important techniques used in predictive analytics.
- Assessing predictive models is essential when building and deploying machine learning models.
- Predictive models can be assessed in several ways, including cross-validation, holdout validation, and A/B testing.
Frequently Asked Questions
What does this course cover?
It covers optimizing randomization and parallelization, the concepts of gradient, descent, and intuition, and assessing predictive models through cross-validation, holdout validation, and A/B testing, with the goal of improving the performance of predictive algorithms and models.
What will I learn to do in this course?
You will learn to optimize the performance of algorithms and models, identify tools for optimizing the performance of machine learning, and assess predictive models when building and deploying them.
How is the course structured?
The course is organized into lessons: Introduction; Optimizing Randomization and Parallelization; Gradient, Descent, and Intuition; Assessing Models and Deployment; and Test Your Knowledge.
Why is assessing predictive models important?
Assessing predictive models is essential when building and deploying machine learning models, and the course looks at several ways to assess them, including cross-validation, holdout validation, and A/B testing.
Professional Certifications and Continuing Education Units (CEUs)
Project Management Institute (PMI®)
Professional Development Units (PDUs): 0.25
KnowledgeCity has been reviewed and approved by the PMI® Authorized Training Partner Program. Users can maintain your PMI credentials by earning PDUs from KnowledgeCity qualified courses in Project Management, Business Management, Leadership and many others.
Society for Human Resource Management (SHRM®)
Professional Development Credits (PDCs): 0.25
KnowledgeCity is approved by SHRM as a Recertification General Provider to offer SHRM-CP or SHRM-SCP professional development credits (PDCs). By taking the courses approved by SHRM, KnowledgeCity can award SHRM Professional Development Credits (PDCs) for HR knowledge and competency programs related to the SHRM Body of Applied Skills and Knowledge™ (the SHRM BASK™).
